Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

Subjects:

Image collected from bank abutment. by Xiaofeng Qu (577153)

AUC scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”

Recall scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…Empirical evaluations conducted on multiple benchmark datasets demonstrate that the proposed method outperforms classical anomaly detection algorithms while surpassing conventional model averaging techniques based on minimizing standard loss functions. …”

GraSPy: an Open Source Python Package for Statistical Connectomics by Benjamin Pedigo (6580352)

“…GraSPy builds on Python’s existing graph and machine learning ecosystem by accepting input from NetworkX and complying with the scikit-learn API. …”

Image3_Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.TIFF by Jian-Kun Song (11711756)

“…</p><p>Results: We identified a total of 39 PRGs, which could distinguish psoriasis samples from normal samples. …”

Image1_Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.TIFF by Jian-Kun Song (11711756)

“…</p><p>Results: We identified a total of 39 PRGs, which could distinguish psoriasis samples from normal samples. …”

Image2_Classification and biomarker gene selection of pyroptosis-related gene expression in psoriasis using a random forest algorithm.TIFF by Jian-Kun Song (11711756)

“…</p><p>Results: We identified a total of 39 PRGs, which could distinguish psoriasis samples from normal samples. …”

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

Table_4_A Comparative Study of Cluster Detection Algorithms in Protein–Protein Interaction for Drug Target Discovery and Drug Repurposing.xlsx by Jun Ma (9393)

“…The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. …”

Table_1_A Comparative Study of Cluster Detection Algorithms in Protein–Protein Interaction for Drug Target Discovery and Drug Repurposing.DOCX by Jun Ma (9393)

“…The topological modules generated by a computational approach are defined as functional clusters. However, the functions inferred for these topological modules extracted from a large-scale molecular interaction network, such as a protein–protein interaction (PPI) network, could differ depending on different cluster detection algorithms. …”

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

“…Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. …”

CEC2017 basic functions. by Tengfei Ma (597633)

“…The optimal individual’s position is updated by randomly selecting from these factors, enhancing the algorithm’s ability to attain the global optimum and increasing its overall robustness. …”

The main steps of the general algorithm. by Dror Mughaz (18017583)

Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems by Dongge Lei (6836915)

“…The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design problems. Experimental results show that the performance of QOBL-ICA is superior to most state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence speed.…”

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy by Ayla Arslan (17943365)

“…</p> <p>The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. …”

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Image_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.TIFF by Hao Chen (5190)

“…The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. …”

Table_1_Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm.XLSX by Hao Chen (5190)

“…The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. …”